A growing number of mobile apps are exploiting smartphone sensors to infer
user behavior, activity, or context. For instance, an app may infer from the
accelerometer that its user is inside a car; another app may infer if the user is
at a dance party from sound, light, and motion information from multiple users.
Regardless of what is being inferred, these apps require training (i.e., the raw
sensor data need to be initially labeled with the ground truth, such as “driving”
or “dance party”). Obtaining labeled data for new mobile sensing apps is proving
to be a “chicken and egg” problem. Users who install such apps are usually not
willing to help with labeling – they demand immediate service. Without a
reasonable amount of labeling, the apps are not able to perform inference, and
are not worth installing. This paper aims to address this problem, helping mobile
apps to bootstrap with just a few users. Our core intuition is that even though
each user may be different, they may exhibit similar patterns on certain sensing
dimensions some of the time. For instance, different users may walk and drive at
different speeds, but certain speeds will indicate driving for all users. These
common patterns could be used as “seeds” to model the new user, and label her
data on all other dimensions. We prototype a technique to automatically extract
the commonalities to seed models for new users and learn a unique personalized
inference model for each user. We evaluate the proposed technique through example
apps and real world data.